SPOM is a new approach to IVM, mimicing some characteristics of oocyte maturation in vivo and substantially improving oocyte developmental outcomes. Adaption of SPOM for clinical application should have significant implications for infertility management and bring important benefits to patients.

BackgroundLifestyle factors have been associated mostly with individual chronic diseases. We investigated the relationship between lifestyle factors (individual and combined) and the co-occurrence of multiple chronic diseases.MethodsCross-sectional analysis of results from the Program of Research on the Evolution of a Cohort Investigating Health System Effects (PRECISE) in Quebec, Canada. Subjects aged 45 years and older. A randomly-selected cohort in the general population recruited by telephone. Multimorbidity (3 or more chronic diseases) was measured by a simple count of self-reported chronic diseases from a list of 14. Five lifestyle factors (LFs) were evaluated: 1) smoking habit, 2) alcohol consumption, 3) fruit and vegetable consumption, 4) physical activity, and 5) body mass index (BMI). Each LF was given a score of 1 (unhealthy) if recommended behavioural targets were not achieved and 0 otherwise. The combined effect of unhealthy LFs (ULFs) was evaluated using the total sum of scores.ResultsA total of 1,196 subjects were analyzed. Mean number of ULFs was 2.6 ± 1.1 SD. When ULFs were considered separately, there was an increased likelihood of multimorbidity with low or high BMI [Odd ratio (95% Confidence Interval): men, 1.96 (1.11-3.46); women, 2.57 (1.65-4.00)], and present or past smoker [men, 3.16 (1.74-5.73)]. When combined, in men, 4-5 ULFs increased the likelihood of multimorbidity [5.23 (1.70-16.1)]; in women, starting from a threshold of 2 ULFs [1.95 (1.05-3.62)], accumulating more ULFs progressively increased the likelihood of multimorbidity.ConclusionsThe present study provides support to the association of lifestyle factors and multimorbidity.

PURPOSE We aimed to develop a consensus-based set of core outcomes specifically for studies in multimorbidity. METHODSWe undertook a consensus study following the COS-STAR (Core Outcome Set-STAndards for Reporting) guidelines for the design and reporting of core outcome sets. A Delphi panel of experts completed a web-based survey with 2 rounds. Panelists were presented with a range of outcomes that had been identified in previous workshops and a related systematic review. They indicated their level of agreement on whether each outcome should be included in the core set using a 5-point Likert scale, and outcomes reaching a prespecified consensus level were included. RESULTSOf 30 individuals invited to be panelists, 26 from 13 countries agreed. All 26 completed both rounds of the survey. The Delphi panel reached consensus on 17 outcomes for inclusion in a core outcome set for multimorbidity (COSmm). The highest-ranked outcomes were health-related quality of life, mental health outcomes, and mortality. Other outcomes were grouped into overarching themes of patient-reported impacts and behaviors (treatment burden, self-rated health, self-management behavior, self-efficacy, adherence); physical activity and function (activities of daily living, physical function, physical activity); consultation related (communication, shared decision making, prioritization); and health systems (health care use, costs, quality of health care). CONCLUSIONSThis consensus study involved a wide range of international experts who identified a large number of outcomes for multimorbidity intervention studies. Our results suggest that quality of life, mental health outcomes, and mortality should be regarded as essential core outcomes. Researchers should, however, also consider the full range of outcomes when designing studies to capture important domains in multimorbidity depending on individual study aims and interventions.

The phosphodiesterase (PDE) family is a group of enzymes that catalyzes the transformation of cyclic nucleotides into 5' nucleotides. Based on rodents, the current mammalian model of PDE distribution in the ovarian follicle predicts Pde3a in the oocyte and Pde4d in the somatic cells. Using bovine as an experimental model, the present results showed that PDE3 was the predominant PDE activity in oocytes. However, cumulus cell cAMP-PDE activity was predominantly resistant to inhibition by 3-isobutyl-methylxantine, indicating PDE8 activity (60% of total PDE activity) and a minor role for PDE4 (<5%). A total of 20% of total oocyte PDE activity was also attributed to PDE8. The PDE activity measurements in mural granulosa cells from 2 to 6 mm in diameter suggest the presence of PDE4 and PDE8. In granulosa cells from follicles >10 mm, total PDE and PDE8 activities along with PDE8A protein level were increased compared with smaller follicles. The RT-PCR experiments showed that cumulus cells expressed PDE8A, PDE8B, and PDE10A. Western blot experiments showed PDE8A, PDE8B, and PDE4D proteins in mural granulosa cells and cumulus-oocyte complexes. PDE8 inhibition using dipyridamole in a dose-dependent manner increased cAMP levels in the cumulus-oocyte complexes and delayed oocyte nuclear maturation. These results are the first to demonstrate the functional presence of PDE8 in the mammalian ovarian follicle. This challenges the recently described cell-specific expression of cAMP-PDEs in the ovarian follicle and the notion that PDE4 is the predominant granulosa/cumulus cell PDE. These findings have implications for our understanding of hormonal regulation of folliculogenesis and the potential application of PDE inhibitors as novel contraceptives.